Hey guys,
we just wanted to let you know that Gremlin is getting smarter and smarter.
We added a new traversal optimizer strategy: AdjacentToIncidentStrategy
<https://issues.apache.org/jira/browse/TINKERPOP3-654> (previously called
HalfStepTraversalStrategy).
*What does it do?*
It looks for typical patterns / step sequences in traversals that involve a
significant performance overhead (especially in OLAP), and replaces them
with "shorter" alternatives.
*A few examples:*
OriginalOptimizedg.V().out().count()g.V().outE().count()
g.V().in().limit(3).count()g.V().inE().limit(3).count()
g.V().values("name").count()g.V().properties("name").count()
g.V().has(__.out())g.V().has(__.outE())g.V().has(__.values())
g.V().has(__.properties())
*What's next?*
Now that we have AdjacentToIncidentStrategy, it's time to create an
IncidentToAdjacentStrategy. No kidding, the other way can be an
optimization too. Consider the following traversal:
g.V().outE().inV().has(__.out("friend")).has(__.outE("workedAt").inV().has("name",
"FooBar LLC"))
Believe me, I've seen a lot of queries like this in the past. But now,
after applying the two aforementioned strategies, we would end up with:
g.V().out().has(__.outE("friend")).has(__.out("workedAt").has("name",
"FooBar LLC"))
Pretty cool, isn't it?
Cheers,
Daniel
